PDF(Pubmed)
Abstract：
OBJECTIVE: Using a large longitudinal sample of adults from the Midlife in the United States (MIDUS) study, the present study extended a recently developed hierarchical model to determine how best to model the accumulation of stressors, and to determine whether the rate of change in stressors or traditional composite scores of stressors are stronger predictors of health outcomes.
METHODS: We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes.
RESULTS: The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change.
CONCLUSIONS: Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.
METHODS: We used factor analysis to estimate a stress-factor score and then, to operationalize the accumulation of stressors we examined five approaches to aggregating information about repeated exposures to multiple stressors. The predictive validity of these approaches was then assessed in relation to different health outcomes.
RESULTS: The prediction of chronic conditions, body mass index, difficulty with activities of daily living, executive function, and episodic memory later in life was strongest when the accumulation of stressors was modeled using total area under the curve (AUC) of estimated factor scores, compared to composite scores that have traditionally been used in studies of cumulative stress, as well as linear rates of change.
CONCLUSIONS: Like endogenous, biological markers of stress reactivity, AUC for individual trajectories of self-reported stressors shows promise as a data reduction technique to model the accumulation of stressors in longitudinal studies. Overall, our results indicate that considering different quantitative models is critical to understanding the sequelae and predictive power of psychosocial stressors from midlife to late adulthood.
摘要：
目的：使用美国中年人（MIDUS）研究的大型纵向样本，本研究扩展了最近开发的分层模型，以确定如何最好地对压力源的积累进行建模，并确定压力源的变化率或压力源的传统综合评分是否是健康结果的更强预测因子。
方法：我们使用因子分析来估计压力因子得分，然后，为了操作压力源的积累，我们研究了五种方法来汇总有关重复暴露于多种压力源的信息。然后根据不同的健康结果评估这些方法的预测有效性。
结果：慢性疾病的预测，身体质量指数,日常生活活动困难，执行功能，当使用估计因子得分的曲线下总面积（AUC）对压力源的积累进行建模时，生活后期的情景记忆最强，与传统上用于累积压力研究的综合评分相比，以及线性变化率。
结论：像内源性的，应激反应性的生物标记，自我报告压力源的个体轨迹的AUC显示出有望作为一种数据减少技术来模拟纵向研究中压力源的积累。总的来说,我们的研究结果表明,考虑不同的定量模型对于理解从中年到成年后期的心理社会应激源的后遗症和预测能力至关重要.
方法：我们使用因子分析来估计压力因子得分，然后，为了操作压力源的积累，我们研究了五种方法来汇总有关重复暴露于多种压力源的信息。然后根据不同的健康结果评估这些方法的预测有效性。
结果：慢性疾病的预测，身体质量指数,日常生活活动困难，执行功能，当使用估计因子得分的曲线下总面积（AUC）对压力源的积累进行建模时，生活后期的情景记忆最强，与传统上用于累积压力研究的综合评分相比，以及线性变化率。
结论：像内源性的，应激反应性的生物标记，自我报告压力源的个体轨迹的AUC显示出有望作为一种数据减少技术来模拟纵向研究中压力源的积累。总的来说,我们的研究结果表明,考虑不同的定量模型对于理解从中年到成年后期的心理社会应激源的后遗症和预测能力至关重要.
